The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Adaptive Dynamic Surface Control for Formations of Autonomous Surface Vehicles With Uncertain Dynamics
Abstract
In this brief, we consider the formation control problem of underactuated autonomous surface vehicles (ASVs) moving in a leader-follower formation, in the presence of uncertainties and ocean disturbances. A robust adaptive formation controller is developed by employing neural network and dynamic surface control technique. The stability of the design is proven via Lyapunov analysis where semiglobal uniform ultimate boundedness of the closed-loop signals is guaranteed. The advantages of the proposed formation controller are that: first, the proposed method only uses the measurements of line-of-sight range and angle by local sensors, no other information about the leader is required for control implementation; second, the developed neural formation controller is able to capture the vehicle dynamics without exact information of coriolis and centripetal force, hydrodynamic damping and disturbances from the environment. Comparative analysis with a model-based approach is given to demonstrate the effectiveness of the proposed method.
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Cites
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Swaroop, D., Hedrick, J. K., Yip, P. P., & Gerdes, J. C. (2000) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2000.880994 · Cited by 2,445
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Self-citationA vision-based formation control framework
Das, A. K., Fierro, R., Kumar, V., Ostrowski, J. P., Spletzer, J., & Taylor, C. J. (2002) · IEEE Transactions on Robotics and Automation · doi:10.1109/tra.2002.803463 · Cited by 1,146
Multilayer neural-net robot controller with guaranteed tracking performance
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A coordination architecture for spacecraft formation control
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Consensus strategies for cooperative control of vehicle formations
Ren, W. (2007) · IET Control Theory & Applications · doi:10.1049/iet-cta:20050401 · Cited by 931
Leader–follower formation control of nonholonomic mobile robots with input constraints
Consolini, L., Morbidi, F., Prattichizzo, D., & Tosques, M. (2008) · Automatica · doi:10.1016/j.automatica.2007.09.019 · Cited by 681
Cited by
Cui, R., Yang, C., Li, Y., & Sharma, S. (2017) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2016.2645699 · Cited by 538
Peng, Z., Wang, J., & Wang, D. (2018) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2017.2699167 · Cited by 382
Self-citationSurvey of deep learning for autonomous surface vehicles in marine environments
Qiao, Y., Yin, J., Wang, W., Duarte, F., Yang, J., & Ratti, C. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3235911 · Cited by 192
Yao, J., Deng, W., & Sun, W. (2017) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2017.2688353 · Cited by 124
Metadata
- Authors
- Peng, Z., Wang, D., Chen, Z., Hu, X., & Lan, W.
- Venue
- IEEE Transactions on Control Systems Technology
- Published
- 2013-03
- Volume
- 21
- Issue
- 2
- Pages
- 513-520
- ISSN
- 1063-6536 (ISSN-L)
- 1558-0865
- OpenAlex
- W2013928382
- Cited by
- 517
- References
- 18
- FWCI
- 28.70
- Type
- journal article
- Language
- en
- Open access
- closed
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